class_docstr: Layer that processes dropout with exponential decayed keep rate during
  training.
  
  :param initial_keep_rate: the initial keep rate of decaying dropout.
  :param decay_interval: the decay interval of decaying dropout.
  :param decay_rate: the decay rate of decaying dropout.
  :param noise_shape: a 1D integer tensor representing the shape of the
      binary dropout mask that will be multiplied with the input.
  :param seed: a python integer to use as random seed.
  :param kwargs: standard layer keyword arguments.
  
  Examples:
      >>> import matchzoo as mz
      >>> layer = mz.contrib.layers.DecayingDropoutLayer(
      ...     initial_keep_rate=1.0,
      ...     decay_interval=10000,
      ...     decay_rate=0.977,
      ... )
      >>> num_batch, num_dim =5, 10
      >>> layer.build([num_batch, num_dim])
docstr: Get the config dict of DecayingDropoutLayer.
docstr_tok: ["Get","the","config","dict","of","DecayingDropoutLayer","."]
code: def get_config(self):
      """Get the config dict of DecayingDropoutLayer."""
      config = {
          "initial_keep_rate": self._initial_keep_rate,
          "decay_interval": self._decay_interval,
          "decay_rate": self._decay_rate,
          "noise_shape": self._noise_shape,
          "seed": self._seed,
      }
      base_config = super(DecayingDropoutLayer, self).get_config()
      return dict(list(base_config.items()) + list(config.items()))
  
file_path: MatchZoo/matchzoo/contrib/layers/decaying_dropout_layer.py
imports: import tensorflow
  from keras import backend
  from keras.engine import Layer
label_desc: Use image node_id 5 for calling the DecayingDropoutLayer obj's underlying member method code with example usage: obj.get_config() and returns: dict